Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mass Spectrometry: Complex Analysis

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material, molecules absorb light depending on the energy required for electronic transitions. As a result...

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Continuous-surface 3D reconstruction from kilometer-range single-photon LiDAR using score-based priors.

Scientific reports·2026

Same author

Human activity recognition at a kilometer range using single-photon LiDAR.

Optics express·2026

Same author

WAFFLE - an automated platform for enhancing the performance of electrochemical biosensors.

Lab on a chip·2026

Same author

Bayesian Multifractal Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

Same author

On-the-fly adaptive SNR protocol to accelerate Brillouin microscopy.

Optics express·2025

Same author

Plug-and-play algorithm for 3D guided video super-resolution of single-photon LiDAR data.

Optics express·2025

Search research articles

Related Experiment Video

Updated: May 24, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery.

Yoann Altmann¹, Abderrahim Halimi, Nicolas Dobigeon

¹University of Toulouse, IRIT/INP-ENSEEIHT/TeSA, Toulouse, France. yoann.altmann@enseeiht.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 21, 2012

Summary

This study introduces a new nonlinear mixing model for hyperspectral image unmixing, addressing complex spectral signatures. The polynomial postnonlinear model and Bayesian methods accurately estimate components from noisy data.

More Related Videos

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Related Experiment Videos

Last Updated: May 24, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

Remote Sensing
Signal Processing
Computer Vision

Background:

Hyperspectral imaging captures detailed spectral information.
Traditional linear unmixing models are insufficient for complex scenarios.
Nonlinear spectral mixing is prevalent in many real-world applications.

Purpose of the Study:

To develop a novel nonlinear mixing model for hyperspectral image unmixing.
To address the limitations of existing linear models in complex spectral environments.
To provide accurate estimation of spectral components from noisy hyperspectral data.

Main Methods:

A polynomial postnonlinear mixing model is proposed.
Bayesian algorithms and optimization techniques are employed for parameter estimation.
Simulations on synthetic and real hyperspectral data are used for evaluation.

Main Results:

The proposed nonlinear model effectively captures complex spectral signatures.
The Bayesian approach provides robust parameter estimation.
Evaluations demonstrate superior performance compared to existing methods.

Conclusions:

The polynomial postnonlinear model is a viable approach for hyperspectral unmixing.
Accurate estimation of spectral components is achievable even with noise.
The developed methods offer improved unmixing performance for real-world hyperspectral data.